Accelerating Supply Chain Velocity With Data Integration

It would be unfortunate if electronics companies were to resist the integration of transactional data, such as geography, delayed shipments, and supplier payment terms, with master data, such as supplier, customer, and inventory management. There is a great wealth of data that can be collected from search engines and marketing and advertising platforms to provide insight into consumer interest and intent.

Such data harvesting should enable more accurate demand forecasts. Adobe, Oracle, SAP, and Salesforce have been working to create platforms that not only combine internal and external data from manufacturers and suppliers, but also structured and unstructured data sources across the Internet from consumers. These unstructured sources bring in insight from social sites like LinkedIn, Twitter, or Facebook. Imagine making a decision to fill a bill of materials to build a smartphone or tablet based on data gleaned from likes or dislikes in social networks or the number of tweets on a specific subject in Twitter.

In 2010, I wrote about using search engines to estimate product demand. Now experts like Arvind J. Singh, co-founder and CEO of Utopia, a global data lifecycle consulting and services firm, suggest mining text and searches, as well as social comments and recommendations in unstructured data, to integrate with master data. Now companies are finding ways to harness all types of marketing data in the raw materials procurement process.

Silo dataI'll resist calling the phenomenon "big-data," the collection of information from internal and external inputs, because I believe the electronics industry went though that in the early 2000s when Hewlett-Packard, Wal-Mart Stores, and Target began requiring suppliers to tag pallets with radio frequency identification (RFID) tags. During the RFID boon, we heard about terabytes of raw data and how IT departments would struggle to determine what to keep or discard.

This next evolution, not revolution, introduces new silos of information. Not just customer and company data stored in CRM and ERP platforms or point-of-sale systems, but data from marketing and advertising platforms that measure sentiment and intent. It will create a better supply chain by improving component forecasts in specific geographic regions.

Integrating silos of data should come as second nature to electronics manufacturers and distributors. Even before the introduction of RFID into the supply chain, electronic components distributors expanded from the United States into Asia/Pacific as brands moved manufacturing to China, Vietnam, and India, looking for cheap labor and lower prices on materials. The more overseas acquisitions companies like Avnet and Arrow made, the more difficult it became to integrate enterprise resource planning (ERP) platforms and inventory management systems. They had to figure out how to combine duplicate product descriptions.

Transactional + master data = insightAccurate master data from inventory management systems, along with company finance, supplier, customer, and employee data, gives buyers information to identify the exact lead times, quantities, and pricing of raw materials for bills of materials and purchase orders. The data also provides information on the performance of suppliers when it comes to quality of parts, delivery, and prices.

Transactional data from geography, delayed shipments, or suppliers that have the shortest or the longest payment terms lets procurement specialists take action based on insights about the brand's top suppliers, which typically get the biggest part of the budget.

Every procurement decision should combine transactional data with master data, according to Singh. He gives electronic components procurement experts three best-practices for combining transactional and master data.

Understand the source and accuracy of the data.

Have access to the aggregate of transactions to analyze and vet the performance of the suppliers to negotiate using the information.

Gain insight into supplier service levels across all component categories.

What do you think? If you use an approach like this, let us know your experience to date.

Mid-size companies, depending on how you define them, are not leveraging data correctly. Most companies want to skin the surface for information, but it's only when you dig deep into the numbers that you find the better answers. Actually, that's where distributors or companies like Adobe and Salesforce could help with their expertise in data, offering a pay-as-you-go service. They already have the systems in place. I think that's the way to support smaller companies with big data, too, enabling them to scale up or down with services as needed. IDC expects the big data technology and services market to grow at a 31.7% CAGR through 2016. With that type of growth, even the small companies will require some sort of service.

Laurie, great post... I'd be interested in your perspective on how mid-sized companies are (or are not) leveraging big-data properly. At first blush and with limited staffing and budget resources, I have to imagine it's an overwhelming task to consider for such companies.

While unions and terminal operators go back and forth on contract negotiations and possible solutions to the slowdown, manufacturers are caught in the middle losing millions of dollars, because they're not able to meet their customers' demand.